Pattern Recognition - Applications and Methods International Conference, ICPRAM 2012 Vilamoura, Algarve, Portugal, February 6-8, 2012 Revised Selected Papers

This edited book includes extended and revised versions of a set of selected papers from the First International Conference on Pattern Recognition (ICPRAM 2012), held in Vilamoura, Algarve, Portugal, from 6 to 8 February, 2012, sponsored by the Institute for Systems and Technologies of Information Control and Communication (INSTICC) and held in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI) and Pattern Analysis, Statistical Modelling and Computational Learning (PASCAL2). The conference brought together researchers, engineers and practitioners interested on the areas of Pattern Recognition, both from theoretical and application perspectives.

Pattern Recognition as an Indicator of Diagnostic Expertise.- Analysis of Factorial Designs with the Consideration of Interactions via the Stepwise Response Refinement Screener (SRRS).- Object Detection with Semi-local Features.- Inferring Time Varying Dynamic Linear Model Parameters from a Mixture Model.- A Performance Evaluation of Mutual Information Estimators for Multivariate Feature Selection.- Modelling and Explaining Online News Preferences.- Combining Graph Seriation and Substructures Mining for Graph Recognition.- Segmentation of Nonstationary Time Series with Geometric Clustering.- A Novel Framework for Nontechnical Losses Detection in Electricity Companies.- Adaptive Features for Object Classification.- SVM-based Feature Selection and Classification for Email Filtering.- Instance Selection Methods and Resampling Techniques for Dissimilarity Representation with Imbalanced Data Sets.- Simplex Decompositions using Singular Values Decomposition.- On the Understanding of the Stream Volume Behavior on Twitter.- Training Selection with Label Propagation for Semi-supervised Land Classification and Segmentation of Satellite Images.- Hyperspectral Imagery Framework for Unmixing and Dimensionality Estimation.